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2022 International Symposium on Semiconductor and Electronic Technology, ISSET 2022

作者： Sun, Wanjun Yang, Lijiao Xie, Jianan Zhang, Xiaxia Sha, Yang Mi, Shuai Lin, Tao College of Automation and Information Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

To meet the demand of the laser application market for semiconductor laser function diversification, this paper proposes a handheld multi-wavelength laser stack packaging module. The 808 nm, 915 nm, and 980 nm semiconductor laser bars are selected for the integrated assembly of the multi-wavelength module. However, with the continuous increase of the optical power density of the integrated packaging module, thermal management has become a key factor affecting the performance and life of the device. In this paper, the finite element analysis method is used to analyze the influence of the arrangement of three laser stripe chips in the stacked packaging structure on the heat dissipation of the multi-wavelength module. The simulation results show that the heat dissipation efficiency of the multi-wavelength module can be improved by 13.74 % by changing the arrangement of chips. A back heat dissipation packaging structure with higher heat dissipation efficiency is proposed, and the influence of chip arrangement and working mode on the thermal characteristics of the device in the new packaging structure is simulated and analyzed. According to the simulation results, a chip arrangement that can make the heat dissipation effect of the multi-wavelength module achieve the best is determined. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Semiconductor lasers

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Differentiator-Based Adaptive H8 Tracking control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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Cluster routing algorithm based on agglomerative hierarchical clustering in wireless sensor networks

Cluster routing algorithm based on agglomerative hierarchica...

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2023 International Conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2023

作者： Li, Hongbing Cui, Hao Ou, Jun Wang, Yuning Key Laboratory of Intelligent Information Processing Control of Chongqing Three Gorges University China Chongqing Key Laboratory of Geological Environment Monitoring Disaster Early Warning in Three Gorges Reservoir Area China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center China

ISBN: (纸本)9781510666238

To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensor networks. WSN is divided into a certain quantity of ring areas based on the energy consumption model. Clustering process is optimized by constraining the distance between clusters and the diameter of clusters to improve the quality of clustering algorithm. Cluster head selection strategy is improved by considering the residual energy of the node and energy consumption in communication with other nodes in one cluster. Simulation results show that CRAAHC algorithm can achieve energy efficiency and routing reliability. © 2023 SPIE.

关键词： Clustering algorithms

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Semi-supervised Speech Lie Detection Algorithm Based on Multiple Features and Adaptive Threshold

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第6期52卷 2015-2021页

作者： Zheng, Shuailong Tao, Huawei Yu, Hang Xi, Ji Zhu, Chunhua Wang, Xuemei Key Laboratory for Grain Information Processing and Control Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou450001 China Computer Information Engineering Changzhou Institute of Technology Jiangsu213000 China

The limited number of labeled samples can significantly affect the accuracy of speech lie detection systems. However, semi-supervised algorithms have been shown to effectively improve detection performance under such constraints. To address this challenge, this paper proposes a semi-supervised speech lie detection algorithm that utilizes multiple features and adaptive thresholds. First, through comparative analysis, this paper identifies 312-dimensional acoustic features and 768-dimensional self-supervised features as the key elements of lie detection. Then, these acoustic features are processed using a bidirectional long short-term memory network (Bi-LSTM), which obtains comprehensive information by analyzing acoustic statistical features in both forward and backward directions. Subsequently, this paper develops a semi-supervised feature fusion module (BSFM) to extract deeper insights from basic features and achieves adaptive feature fusion through targeted training. In addition, based on a detailed analysis of the distribution characteristics of lie detection samples, this paper proposes an improved Freematch algorithm to better utilize unlabeled data. Experimental results show that the proposed algorithm outperforms most existing methods on the self-built H-Wolf dataset and the Columbia/SRI/Colorado Corpus (CSC). © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Semi-supervised learning

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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

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Computers, Materials & Continua 2024年第9期80卷 4497-4512页

作者： Tianqing Yuan Na Li Hao Sun Sen Tan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Department of Electrical Engineering Northeast Electric Power UniversityJilin132012China College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China Center for Research on Microgrids Department of EnergyAalborg UniversityAalborg9220Denmark

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

关键词： Lithium-ion batteries state of charge state of health cyber-attacks genetic algorithm back propagation neural network

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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Research on Safety Integrity Level Assessment for Stage Machinery of Temporary Performance Site

Research on Safety Integrity Level Assessment for Stage Mach...

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2023 International Conference on Culture-Oriented Science and Technology, CoST 2023

作者： Hu, Yubin Su, Zhibin Wang, Zixiao Wang, Huiqin School of Information and Communication Engineering Communication University of China China Ministry of Culture and Tourism Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China

ISBN: (纸本)9798350357998

The safety assessment of stage performing arts equipment for the temporary site is an urgent task to be solved. In this paper, the lifting equipment is chosen for risk analysis, safety circuit identification and safety integrity level (SIL) calculation. The Hazard and Operability Study(HAZOP) and Layer Of Protection Analysis(LOPA)-based method is studied to get the expected SIL level of the related safety loops. In addition, the fault tree and Markov model methods are used to verify the actual SIL level under the existing loop structure respectively. This study provides the theory and data basis for the development of safety requirements and evaluation standards for the temporary theater of stage machinery, which has certain theoretical significance and application value. © 2023 IEEE.

关键词： Risk analysis

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Multi-scale Region-level Graph Convolutional Network for Unsupervised SAR image Change Detection

Multi-scale Region-level Graph Convolutional Network for Uns...

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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Zhu, Jingxing Liu, Rui Wang, Feng You, Hongjian Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510680012

With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches. © 2024 SPIE.

关键词： Change detection

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The Potential of Multi-Agent Consensus Equilibrium for Synthetic Aperture Radar processing 15

The Potential of Multi-Agent Consensus Equilibrium for Synth...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Fan, Yizhe Li, Jie Wang, Kun Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

Multi-agent consensus equilibrium(MACE) mechanism is a generalization of popular used PnP-ADMM method in computational imaging. We propose a novel SAR processing framework based on MACE mechanism, named by MACE-SAR. The proposed framework offers the possibility to unify various SAR processing agents of both data-driven learned priors and physics-based regularization priors, enabling the integration of diverse functions and enhance the image reconstruction quality. We exemplifies how the MACE-SAR framework can be customized to combine SAR image formation, feature enhancement, image denoising, target detection, etc. The effectiveness of the framework is demonstrated through experiments conducted on simulated and Gaofen-3 satellite real data. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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